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Student Early Warning Systems: An Introduction and Best Practices

February 16th, 2016

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All students have different starting points.

It’s a fact not lost upon district education leaders, many of whom have devoted their careers to pursuing strategic initiatives for improving student achievement.

While they enjoy victories arising from successful student outcomes, instructional leaders yearn to have all students accomplish their goals. They want to see all students struggling with issues like poor academic performance and absenteeism persevere and graduate. But how exactly can you bring up the bottom?

What is a Student Early Warning System?

Much like physicians, educators continually evaluate the academic health of their student body. Early detection and treatment are vital to an at-risk student’s academic well-being, as dropping out represents the culmination of a gradual disengagement from school. Youths who are at-risk typically begin sliding down a slope toward dropout long before high school age.

Today district education leaders recognize the myriad benefits of conducting early intervention to reverse the course of this downward slope. An early warning system refers to a systematic framework for identifying students who are at increased risk for academic failure. An effective system flags at-risk students, tracks interventions and analyzes associations between interventions and subsequent student outcomes.

To achieve college- and career-readiness, students must navigate key transition points and develop academic behaviors reflecting an understanding of how to succeed at school. Early warning systems are designed to aid navigation efforts for students who have lost their way.

The History of Early Warning Systems

Early warning systems date back to the late 1980s, when initial efforts in identification focused on conducting interviews and compiling checklists. Schools would interview recent dropouts in search of risk trends or measure students against checklists of predictive risk factors in hopes of determining who was at-risk. While these methods produced modest results, they were flawed by their need to draw generalizations and by frequent misclassification of students.

Although early warning systems remain developmental, their execution has improved considerably during the last decade. Expanding research has identified predictive risk factors like declining attendance and underdeveloped reading skills that portend a student is on the wrong path. Meanwhile, educators now understand academic failure takes root at early ages. Still, the need for continued progress remains critical – recent estimates indicate approximately one-quarter of public high school students don’t graduate.

Eight Best Practices for Developing an Early Warning System

The following represent eight best-practice steps for implementing an effective early warning system within your district:

Step 1. Create a District Leadership Team

Your early warning system will experience greater success if you establish a district leadership team to oversee development and implementation. Comprised of educators as well as community and business leaders, your team should include at least one representative from each district building. The leadership team should spearhead tasks ranging from reviewing your district’s current student intervention program to recruiting necessary talent for incorporating a revamped early warning system to establishing buy-in among staff, parents and students.

Step 2. Identify Key Indicators of At-Risk Students

While no single pathway for student disengagement exists, common patterns defining at-risk students have emerged. Begin identifying at-risk students during elementary school and pay particular attention to key transition points (shifts from elementary to middle and middle to high school). Traditional indicators include:

  • Below grade level skills in mathematics
  • Below grade level skills in reading
  • Poor attendance
  • History of behavioral difficulties
  • Retention

These “effect” factors, however, often fail to produce data leading to successful interventions. Consequently, district leaders should also monitor “status” and “causal” indicators – many of which don’t appear in traditional early warning system models.

Step 3. Identify “Status” Indicators of At-Risk Students

Although less commonly used, “status” indicators generally prove more useful than “effect” factors for facilitating intervention for at-risk students. These indicators include:

  • English learner
  • Learning disability
  • Socio-economically disadvantaged
  • Low parental education
  • Homeless/foster care
  • School mobility
  • Involved in justice system

Step 4. Identify “Causal” Indicators of At-Risk Students

Similar to “status” factors, “causal” indicators have proven effective for identifying at-risk students in need of intervention. These indicators include:

  • Identified mental illness in student/parents
  • Alcohol/drug abuse
  • Neurobiological conditions
  • History of bullying or being bullied
  • Poor social skills and lack of meaningful relationships
  • Low self-esteem
  • Lack of persistence in managing tasks

Step 5. Identify Assets and Protective Factors

When creating an early warning system model utilizing analytics, those factors deemed inverse of at-risk students within your district should also be considered. These protective factors range from strong attendance to supportive home life to a high degree of persistence in managing tasks.

Step 6. Develop, Purchase or Enhance a Comprehensive Data System

Data drives the future of student learning. An early warning system demands a robust platform to store and report all student data in effective ways. A proper data and assessment management system maintains information on individual students and helps determine which students are at-risk and to what degree – all while keeping data easily accessible for district administration. Illuminate Education’s DnA System represents a powerful tool for designing and monitoring your district’s early warning system.

Step 7. Conduct Longitudinal Data Analysis of Your Indicators

Once you have historical data on your target students, identify past students in your identified risk categories. Examine the data on these students and determine their status on the various indicators at key transition points in their careers based on historical data. Then set local norms or cut scores based on this analysis.

Step 8. Build Your Early Warning System

The time has come to create your early warning system’s model within your district’s data system, finalizing processes for monitoring and intervening with at-risk students. Determine methodology for on-going data collection, and establish specific systems for developmental levels and transition points. Categories within a comprehensive early warning system include:

  • Reading achievement/trends
  • Math achievement/trends
  • Course grades/trends
  • Behavior
  • Attendance
  • Assets/protective factors
  • Status/causal variables

Ultimately, your district’s ability to establish an effective early warning system will mean the difference between academic success and failure for many at-risk students. When you can intervene and aid those students in need, you strengthen your entire district.


At Illuminate Education we intend to be your school district’s comprehensive provider of Web-based products and services offering innovative data solutions. Serving the K-12 education market, our turnkey data-focused software and services assist more than 1,200 school districts across the United States.

Ready to discover your one-stop-shop for all your district’s educational data needs? We’re here to talk.

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